Antimicrobial peptides from a wide spectrum of insects possess potent microbicidal properties against microbial-related diseases. In this study, seven new gene fragments of three types of antimicrobial peptides were obtained from Hermetia illucens (L), and were named cecropinZ1, sarcotoxin1, sarcotoxin (2a), sarcotoxin (2b), sarcotoxin3, stomoxynZH1, and stomoxynZH1(a). Among these genes, a 189-basepair gene (stomoxynZH1) was cloned into the pET32a expression vector and expressed in the Escherichia coli as a fusion protein with thioredoxin. Results show that Trx-stomoxynZH1 exhibits diverse inhibitory activity on various pathogens, including Gram-positive bacterium Staphylococcus aureus, Gram-negative bacterium Escherichia coli, fungus Rhizoctonia solani Khün (rice)-10, and fungus Sclerotinia sclerotiorum (Lib.) de Bary-14. The minimum inhibitory concentration of Trx-stomoxynZH1 is higher against Gram-positive bacteria than against Gram-negative bacteria but similar between the fungal strains. These results indicate that H. illucens (L.) could provide a rich source for the discovery of novel antimicrobial peptides. Importantly, stomoxynZH1 displays a potential benefit in controlling antibiotic-resistant pathogens.

Soybean is a major source of protein for human consumption and animal feed. Releasing new cultivars with high nutritional value is one of the major goals in soybean breeding. To achieve this goal, genome-wide association studies of seed amino acid contents were conducted based on 249 soybean accessions from China, US, Japan, and South Korea. The accessions were evaluated for 15 amino acids and genotyped by sequencing. Significant genetic variation was observed for amino acids among the accessions. Among the 231 single nucleotide polymorphisms (SNPs) significantly associated with variations in amino acid contents, fifteen SNPs localized near 14 candidate genes involving in amino acid metabolism. The amino acids were classified into two groups with five in one group and seven amino acids in the other. Correlation coefficients among the amino acids within each group were high and positive, but the correlation coefficients of amino acids between the two groups were negative. Twenty-five SNP markers associated with multiple amino acids can be used to simultaneously improve multi-amino acid concentration in soybean. Genomic selection analysis of amino acid concentration showed that selection efficiency of amino acids based on the markers significantly associated with all 15 amino acids was higher than that based on random markers or markers only associated with individual amino acid. The identified markers could facilitate selection of soybean varieties with improved seed quality.

Recent advances in genomic technologies have generated data on large-scale protein–DNA interactions and open chromatin regions for many eukaryotic species. How to identify condition-specific functions of transcription factors using these data has become a major challenge in genomic research. To solve this problem, we have developed a method called ConSReg, which provides a novel approach to integrate regulatory genomic data into predictive machine learning models of key regulatory genes. Using Arabidopsis as a model system, we tested our approach to identify regulatory genes in data sets from single cell gene expression and from abiotic stress treatments. Our results showed that ConSReg accurately predicted transcription factors that regulate differentially expressed genes with an average auROC of 0.84, which is 23.5–25% better than enrichment-based approaches. To further validate the performance of ConSReg, we analyzed an independent data set related to plant nitrogen responses. ConSReg provided better rankings of the correct transcription factors in 61.7% of cases, which is three times better than other plant tools. We applied ConSReg to Arabidopsis single cell RNA-seq data, successfully identifying candidate regulatory genes that control cell wall formation. Our methods provide a new approach to define candidate regulatory genes using integrated genomic data in plants.

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Micro-exons are a set of ultrashort exons with lengths ≤ 51 nucleotides. Our previous study revealed that micro-exons were enriched in AP2 domains and K-box domains, which are crucial components of AP2/ERF (APETALA2/ethylene-responsive element-binding protein) and MADS-box (an acronym of MCM1, AGAMOUS, DEFICIENS and SRF) genes, respectively. In this study, we analyzed micro-exons in the AP2/ERF family from 63 species and demonstrated that 76.8% of micro-exons are concentrated in AP2 domains. Most micro-exons appeared in the AP2 subfamily of all the terrestrial plants, but not algae. In addition, micro-exons and AP2 domains are conserved and under negative selection. The MIKC gene is a typical MADS-box gene family in terrestrial plants and includes one MADS-box domain and one K-box domain. A total of 92.3% of micro-exons were observed in K-box domains, and two micro-exons usually encoded a region of K-box domain, which is the key to MADS-box protein polymerization. Furthermore, the micro-exons of the K-box domain had higher ratios of nonsynonymous mutations than those of the AP2 domains. Overall, here we explored the relationships and differences among micro-exons in AP2/ERF and MADS families, and revealed potential functional roles of micro-exons in these domains.

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